Existing distributed database architectures take a very general view of the organization of data, namely, that the principal operations on the data are queries that allow multiple clients to concurrently access, search, and update data, typically without complex computations. In order to facilitate this general view of data, databases provide complex and expensive serialization techniques to ensure that when multiple clients read and write respectively from and to the same data, the correct results are delivered. For example, the prior art teaches various complex locking mechanisms on the data to avoid intervening, conflicting updates and to achieve serialization. These locking mechanisms allow multiple clients to concurrently write to a particular item in a database, but require overhead elements that reduce access speed and increase computational complexity. Further, the architectures provide no explicit separation of data attributes to ensure that sensitive personal data are compartmentalized and isolated in the network architecture. The impact of these attributes becomes more severe as the size of the database and the frequency of accesses to the data increase. Prior art serialization techniques, therefore, suffer problems with scalability, security, and concurrency.
Existing mechanisms for mitigating the impacts of serialization such as logical control of query queues, prioritization, and nesting database structures still leave a lot to be desired in terms of scalability, security, and concurrency.
In addition to distributed database architectures, a variety of distributed sensor architectures also exist in the prior art. These sensor architectures generally fall into two groups. The first group focuses on application specific architectures that use off-the-shelf database technology for information storage and retrieval. The overhead elements in off-the-shelf database technology that are necessary to ensure atomicity reduce scalability, security, and concurrency as described previously.
The second group includes ad-hoc and self-organizing sensor networks that are concerned with management, communication, and synchronization among and between sensors. These architectures introduce new complexities concerned with distributed management of data and suffer from the same fundamental limitations of atomicity, serialization, and concurrency control. Further, there are many practical applications of distributed sensors that do not require sensors to communicate or interact with one another. For these applications the additional costs of organizing distributed sensor management, communication and synchronization are unwarranted and limit concurrency and scaling to large networks.
Thus, the problem with former distributed sensor architectures is that they add unnecessary computational overhead to the acquisition, archiving, and analysis of data provided by the networked sensors. The magnitude of this overhead increases dramatically as the number of sensors increases. In other words, prior art methods for integrating sensor networks are serialized and therefore poorly scalable. Another problem inherent in prior art sensor networks is a lack of security for the information that is delivered by sensors to the server.
Accordingly, a system and a method are desired for acquiring, archiving, and analyzing sensor data via a network of distributed sensors and servers that avoids complex mechanisms for serialization, atomicity, locking protocols, and concurrency control and that inherently protects sensitive data.